The main contribution of this paper is extending the interactive modulation mechanism to RWSR with the help of metric learning. The evaluation includes three aspects. First, we evaluate the estimation ability of our UDEM to present that the proposed metric learning strategy can well perform degradation sore estimation in an unsupervised manner. Second, we evaluate the restoration performance without manually adjusting the degradation score estimated by UDEM. This aims to present that our MM-RealSR can achieve state-of-the-art RWSR performance. Third, we compare our MM-RealSR with the recent modulation SR methods to present that existing modulation methods can not deal with real-world scenarios.
Since AIM19 and RealSRSet datasets provide a paired validation set, we compute the learned perceptual image patch similarity (LPIPS) [1]} and deep image structure and texture similarity (DISTS) [2]} as the evaluation items. We also adopt a non-reference image quality assessment (IQA) metric (i.e., NIQE [3]}) for quantitative evaluation.
